Datenbanksysteme für Business, Technologie und Web最新文献_第10页

Datenbanksysteme in Büro, Technik und Wissenschaft, GI-Fachtagung, Kaiserslautern, 6.-8. März 1991, Proceedings 办公室工程和科学使用的数据库系统谢谢

Datenbanksysteme für Business, Technologie und Web Pub Date : 1900-01-01 DOI: 10.1007/978-3-642-76530-8

Hans-Jürgen Appelrath

引用次数: 0

Charakteristiken des Komplex-Objekt-Begriffs und Ansätze zu dessen Realisierung 独特的特性和适应方法

Datenbanksysteme für Business, Technologie und Web Pub Date : 1900-01-01 DOI: 10.1007/978-3-642-70284-6_27

B. Mitschang

引用次数: 9

From Natural Language Questions to SPARQL Queries: A Pattern-based Approach 从自然语言问题到SPARQL查询:基于模式的方法

Datenbanksysteme für Business, Technologie und Web Pub Date : 1900-01-01 DOI: 10.18420/btw2019-18

Nadine Steinmetz, Ann-Katrin Arning, K. Sattler

{"title":"From Natural Language Questions to SPARQL Queries: A Pattern-based Approach","authors":"Nadine Steinmetz, Ann-Katrin Arning, K. Sattler","doi":"10.18420/btw2019-18","DOIUrl":"https://doi.org/10.18420/btw2019-18","url":null,"abstract":"Linked Data knowledge bases are valuable sources of knowledge which give insights, reveal facts about various relationships and provide a large amount of metadata in well-structured form. Although the format of semantic information – namely as RDF(S) – is kept simple by representing each fact as a triple of subject, property and object, the access to the knowledge is only available using SPARQL queries on the data. Therefore, Question Answering (QA) systems provide a user-friendly way to access any type of knowledge base and especially for Linked Data sources to get insight into the semantic information. As RDF(S) knowledge bases are usually structured in the same way and provide per se semantic metadata about the contained information, we provide a novel approach that is independent from the underlying knowledge base. Thus, the main contribution of our proposed approach constitutes the simple replaceability of the underlying knowledge base. The algorithm is based on general question and query patterns and only accesses the knowledge base for the actual query generation and execution. This paper presents the proposed approach and an evaluation in comparison to state-of-the-art Linked Data approaches for challenges of QA systems.","PeriodicalId":421643,"journal":{"name":"Datenbanksysteme für Business, Technologie und Web","volume":"372 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132809078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

BiZTalk Server 2000: Business Process Management for the Internet BiZTalk Server 2000: Internet的业务流程管理

Datenbanksysteme für Business, Technologie und Web Pub Date : 1900-01-01 DOI: 10.1007/978-3-642-56687-5_40

J. Klein

引用次数: 0

Compaction of Large Class Hierarchies in Databases for Chemical Engineering 化工数据库中大型类层次结构的压缩

Datenbanksysteme für Business, Technologie und Web Pub Date : 1900-01-01 DOI: 10.1007/978-3-642-60119-4_21

Markus Baumeister, M. Jarke

引用次数: 6

Benchmarking the Second Generation of Intel SGX for Machine Learning Workloads 对第二代英特尔SGX机器学习工作负载进行基准测试

Datenbanksysteme für Business, Technologie und Web Pub Date : 1900-01-01 DOI: 10.18420/BTW2023-44

Adrian Lutsch, Gagandeep Singh, Martin Mundt, R. Mogk, Carsten Binnig

引用次数: 0

Efficient Data-Parallel Cumulative Aggregates for Large-Scale Machine Learning 大规模机器学习的高效数据并行累积聚合

Datenbanksysteme für Business, Technologie und Web Pub Date : 1900-01-01 DOI: 10.18420/btw2019-17

Matthias Boehm, A. Evfimievski, B. Reinwald

{"title":"Efficient Data-Parallel Cumulative Aggregates for Large-Scale Machine Learning","authors":"Matthias Boehm, A. Evfimievski, B. Reinwald","doi":"10.18420/btw2019-17","DOIUrl":"https://doi.org/10.18420/btw2019-17","url":null,"abstract":"Cumulative aggregates are often overlooked yet important operations in large-scale machine learning (ML) systems. Examples are prefix sums and more complex aggregates, but also preprocessing techniques such as the removal of empty rows or columns. These operations are challenging to parallelize over distributed, blocked matrices—as commonly used in ML systems—due to recursive data dependencies. However, computing prefix sums is a classic example of a presumably sequential operation that can be efficiently parallelized via aggregation trees. In this paper, we describe an efficient framework for data-parallel cumulative aggregates over distributed, blocked matrices. The basic idea is a self-similar operator composed of a forward cascade that reduces the data size by orders of magnitude per iteration until the data fits in local memory, a local cumulative aggregate over the partial aggregates, and a backward cascade to produce the final result. We also generalize this framework for complex cumulative aggregates of sum-product expressions, and characterize the class of supported operations. Finally, we describe the end-to-end compiler and runtime integration into SystemML, and the use of cumulative aggregates in other operations. Our experiments show that this framework achieves both high performance for moderate data sizes and good scalability.","PeriodicalId":421643,"journal":{"name":"Datenbanksysteme für Business, Technologie und Web","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131178361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

FAIR is not enough - A Metrics Framework to ensure Data Quality through Data Preparation 公平是不够的-通过数据准备确保数据质量的指标框架

Datenbanksysteme für Business, Technologie und Web Pub Date : 1900-01-01 DOI: 10.18420/BTW2023-61

Valerie Restat, Meike Klettke, U. Störl

引用次数: 0

Regelorientierte Erzeugung von Karten-Entwürfen auf geowissenschaftlichen Datenbanken 基于规则在地球科学数据库中制作测绘图

Datenbanksysteme für Business, Technologie und Web Pub Date : 1900-01-01 DOI: 10.1007/978-3-642-72617-0_16

Michael Drawin, K. Neumann, H. Ehrich

引用次数: 2

Dokumentenmodell und automatische Klassifikation im Bürodokumentenarchiv MULTOS 我们采用了文件型号，包括马尔图图书馆采用的自动分类系统

Datenbanksysteme für Business, Technologie und Web Pub Date : 1900-01-01 DOI: 10.1007/978-3-642-72617-0_6

Helmut Eirund, Klaus Kreplin

引用次数: 0